Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study.
Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study.
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作者：
Dave D;Erraguntla M;Lawley M;DeSalvo D;Haridas B;McKay S;Koh C
对即将发生的低血糖事件发出预测性警报，使1型糖尿病患者能够采取预防措施，避免严重后果。本研究旨在开发一种误警率低、灵敏度和特异度高、对新患者和时间段具有良好的泛化能力的低血糖事件预测模型。通过关注持续的降糖事件(定义为至少15分钟内血糖值低于70 mg/dL)，探索了性能改善。考虑了两种不同的建模方法：(1)直接预测持续低血糖事件的基于分类的方法；(2)基于回归的预测范围内多个时间点的血糖预测以及随后对持续低血糖的推断。为了解决模型的泛化和稳健性，考虑了两种不同的验证机制：(1)基于患者的验证(在新患者上评估模型的性能)；(2)基于时间的验证(在新的时间段评估模型的性能)。这项研究利用了110名患者在30-90天内的数据，包括正常生活条件下160万个连续的血糖监测值。该模型准确地预测了持续事件，在30分钟和60分钟的预测期内具有97%的灵敏度和特异度。误警率保持在<；25%。结果在基于患者和基于时间的验证策略中是一致的。提供集中于持续性事件而不是所有低血糖事件的警报可降低误警率，并提高敏感性和特异性。它还产生了对新患者和时间段具有更好普适性的模型。
Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences. This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods. Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods). This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies. Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.